Recent developments have increased the need for computerized analysis of biological images. Some of these types of biological images contain so much visual information, referred to as “noise,” that most standard image computer analysis procedures cannot be applied to accurately detect desired objects. One example of such a noisy images are those of human hair and skin obtained through a microscope. The variations in skin texture, color, surface features, etc. make most image analysis procedures difficult to apply or provide inaccurate results. Of particular interest is the ability to analyze skin images to determine follicular counts and densities for use in hair transplant surgeries.
Qureshi and Bodduluri [1] developed a follicle detection model using contour. With their technique, a contour is drawn around the circumference of any detected possible follicle instance. If the contour has one concave hull, there are two follicles grown from same root. So the contour will look like a triangle with a concave hull. Similarly two concave hulls indicate three follicle units and so on. Unfortunately this method is only applicable to images where the follicle units does not overlap each other and where the follicle units is in good regular geometric shape like hair. If two or more follicles overlap or are crossed over each other, there will be more concave hulls, which will increase the error in counting of follicles. Also, if the image contains irregular shaped follicle units, the method will perceive more concave hulls on the contour.
TrichoScan is another system proposed by Hoffmann and Rolf [2] to count hairs on scalp images. This method detects straight lines on an image. These straight lines are considered to be individual hairs. To ensure accuracy, before the input image is acquired, a patient's hair is subject to a number of chemical treatment steps. These steps make the hair straight and prepares it for image analysis. After treatment and image acquisition, each straight line in the image is considered a hair instance in the image. This method is useful for some standard cases where the hair must be straight and hair edges must be smooth. However, if some hairs are crossed or overlapped with each other, there may be more hair lines counted than actually exist, or other anomalies in the data can occur.
Lempitsky and Zisserman [3] developed a supervised learning model to count objects in microscopic images. This method works well if there is similarity among the trained sets and input sets. Variation between trained sets and real input sets will cause errors. Unfortunately, most follicles in skin images are not similar and vary in size, color and geometric shape. So the learning method of Lempitsky and Zisserman is not very applicable and would require significant refinement to address the problem of follicular analysis of an image.
Thus, it can be seen that there is a particular need to be able to detect and count the number of follicles in a microscopic image of the scalp. An analysis to obtain an exact count of follicles in a given sample area is difficult not only because such images are noisy, but also the follicle shapes are nonstandard. Follicle objects can usually be detected in an image utilizing analysis based on RGB color codes. Unfortunately, follicles also overlapped, three or four of them can be tied together or growing from same root and fan out to different directions. Thus, distinguishing individual follicles or other structures in a visual image can be problematic.